Andrea Acquaviva

Dynamic voltage scaling and power management for portable systems

Portable systems require long battery lifetime while still delivering high performance. Dynamic voltage scaling (DVS) algorithms reduce energy consumption by changing processor speed and voltage at run time depending on the needs of the applications running. Dynamic power management (DPM) policies trade off the performance for the power consumption by selectively placing components into low-power states. In this work we extend the DPM model with a DVS algorithm, thus enabling larger power savings. We test our approach on MPEG video and MP3 audio algorithms running on the SmartBadge portable device. Our results show savings of a factor of three in energy consumption for combined DVS and DPM approaches.

Supporting Task Migration in Multi-Processor Systems-on-Chip: A Feasibility Study

With the advent of multi-processor systems-on-chip, the interest in process migration is again on the rise both in research and in product development. New challenges associated with the new scenario include increased sensitivity to implementation complexity, tight power budgets, requirements on execution predictability, and the lack of virtual memory support in many low-end MPSoCs. As a consequence, effectiveness and applicability of traditional transparent migration mechanisms are put in discussion in this context. Our paper proposes a task management software infrastructure that is well suited for the constraints of single chip multiprocessors with distributed operating systems. Load balancing in the system is maintained by means of intelligent initial placement and task migration. We propose a user-managed migration scheme based on code check pointing and user-level middleware support as an effective solution for many MPSoC application domains. In order to prove the practical viability of this scheme, we also propose a characterization methodology for task migration overhead. We derive the minimum execution time following a task migration event during which the system configuration should be frozen to make up for the migration cost

Energetic sustainability of routing algorithms for energy-harvesting wireless sensor networks

A new class of wireless sensor networks that harvest power from the environment is emerging because of its intrinsic capability of providing unbounded lifetime. While a lot of research has been focused on energy-aware routing schemes tailored to battery-operated networks, the problem of optimal routing for energy harvesting wireless sensor networks (EH-WSNs) has never been explored. The objective of routing optimization in this context is not extending network lifetime, but maximizing the workload that can be autonomously sustained by the network. In this work we present a methodology for assessing the energy efficiency of routing algorithms for networks whose nodes drain power from the environment. We first introduce the energetic sustainability problem, then we define the maximum energetically sustainable workload (MESW) as the objective function to be used to drive the optimization of routing algorithms for EH-WSNs. We propose a methodology that makes use of graph algorithms and network simulations for evaluating the MESW starting from a network topology, a routing algorithm and a distribution of the environmental power available at each node. We present a tool flow implementing the proposed methodology and we show comparative results achieved on several routing algorithms. Experimental results highlight that routing strategies that do not take into account environmental power do not provide optimal results in terms of workload sustainability. Using optimal routing algorithms may lead to sizeable enhancements of the maximum sustainable workload. Moreover, optimality strongly depends on environmental power configurations. Since environmental power sources change over time, our results prompt for a new class of routing algorithms for EH-WSNs that are able to dynamically adapt to time-varying environmental conditions.

Low Power Control Techniques For TFT LCD Displays

Display power consumption is often the most significant contributor to the overall power budget for many portable devices. Traditionally, liquid crystal display (LCD) power minimization has focused on technology and circuit design. In this paper we take an orthogonal approach, and we introduce several software-only techniques for LCD dynamic power management, which do not require any hardware changes on existing LCDs and their controllers. The power savings achieved are significant: from 40% (with no perceivable image degradation) to 60% (with significant, but tolerable degradation) of total system power, measured on a prototype wearable system platform.

Energy characterization of embedded real-time operating systems

In this paper we propose a methodology to analyze the energy overhead due to the presence of an embedded operating system in a wearable device. Our objective is to determine the key parameters affecting the energy consumption of the RTOS allowing the development of more efficient OS-based power management policies. To achieve this target, we propose a characterization strategy that stimulates the RTOS both at the kernel and at the I/O driver level by analyzing various OS-related parameters. Our analisys focus in particular on the relationship between energy consumption and processor frequency characterizing the different functionalities of an RTOS, suggesting a way to develop effective OS-aware energy optimization policies based on variable voltage and frequency. Experimental results are presented for eCos, an open-source embedded OS ported and installed on a prototype of wearable device, the HP SmartBadgeIII.

Remote power control of wireless network interfaces

This paper presents a new power management technique aimed at increasing the energy efficiency of client-server multimedia applications running on wireless portable devices. We focus on reducing the energy consumption of the wireless network interface of the client by allowing the remote server to control the power configuration of the network card depending on the workload. In particular, we exploit server knowledge of the workload to perform an energy-efficient traffic reshaping, without compromising on the quality of service. We tested our methodology on the SmartBadge IV wearable device running an MPEG4 streaming video application. Using our technique we measured energy savings of more than 67% compared to no power management being used on the WLAN interface. In addition, we save as much as 50% of energy with respect to the standard 802.11b power management. All of the energy savings are obtained with no performance loss on the video playback.

Application-specific power-aware workload allocation for voltage scalable MPSoC platforms

In this paper, we address the problem of selecting the optimal number of processing cores and their operating voltage/frequency for a given workload, to minimize overall system power under application-dependent QoS constraints. Selecting the optimal system configuration is non-trivial, since it depends on task characteristics and system-level interaction effects among the cores. For this reason, our QoS-driven methodology for power aware partitioning and frequency selection is based on functional, cycle-accurate simulation on a virtual platform environment. The methodology, being application-specific, is demonstrated on the DES (data encryption system) algorithm, representative of a wider class of streaming applications with independent input data frames and regular work-load.

An adaptive algorithm for low-power streaming multimedia processing

This paper addresses the problem of power consumption in multimedia system architectures and presents an algorithmic optimization technique to achieve the goal of power reduction in the context of real time processing. The technique is based on a mixed speed-setting and shutdown policy. We address the problem from both a theoretical and practical point of view, by presenting a power efficient implementation of a MPEG-layer3 real-time decoder algorithm designed for wearable devices as a case study. The target system is the Hewlett-Packards SmartBadgeIII prototype of wearable system based on the StrongARM1100 processor. Theoretical analysis as well as quantitative results of power measurements are provided to show the effectiveness of this technique. The experimental set-up is also described.

A control theoretic approach to energy-efficient pipelined computation in MPSoCs

In this work, we describe a control theoretic approach to dynamic voltage/frequency scaling (DVFS) in a pipelined MPSoC architecture with soft real-time constraints, aimed at minimizing energy consumption with throughput guarantees. Theoretical analysis and experiments carried out on a cycle-accurate, energy-aware, and multiprocessor simulation platform are provided. We give a dynamic model of the system behavior which allows to synthesize linear and nonlinear feedback control schemes for the run-time adjustment of the core frequencies. We study the characteristics of the proposed techniques in both transient and steady-state conditions. Finally, we compare the proposed feedback approaches and local DVFS policies from an energy consumption viewpoint.

Multi-processor operating system emulation framework with thermal feedback for systems-on-chip

Multi-Processor System-On-Chip (MPSoC) can provide the performance levels required by high-end embedded applications. However, they do so at the price of an increasing power density, which may lead to thermal runaway if coupled with low-cost packaging and cooling. Hence, mechanisms to efficiently evaluate the effectiveness of advanced thermal-aware operating-system (OS) strategies (e.g. task migration) onto the available MPSoC hardware are needed. In this paper, we propose a new MPSoC OS emulation framework that enables the study of thermal management strategies at the architectural- and OS-levels with the help of a standard FPGA. This framework includes the hardware and software components needed to accurately model complex MPSoCs architectures, and to test the effects of run-time thermal management strategies at the OS/middleware level with real-life inputs. Our results show that migration overhead is negligible w.r.t. temperature timings, enabling the development of thermal-aware migration strategies. Moreover, the effectiveness of the monitoring and feedback mechanism provides an emulation performance only ten times slower than real time.